{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d1a189a7-f7a3-4f52-aced-ef209c8f2e20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
      "\u001b[1m17464789/17464789\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 3us/step \n",
      "x_train.shape= (25000,)\n",
      "y_train.shape= (25000,)\n",
      "x_test.shape= (25000,)\n",
      "y_test.shape= (25000,)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "imdb=tf.keras.datasets.imdb\n",
    "(x_train,y_train),(x_test,y_test)=imdb.load_data(num_words=4000)\n",
    "print(\"x_train.shape=\",x_train.shape)\n",
    "print(\"y_train.shape=\",y_train.shape)\n",
    "print(\"x_test.shape=\",x_test.shape)\n",
    "print(\"y_test.shape=\",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b925417-4b7e-42df-8113-3debf9c9f604",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "序列填充前的第一个元素：\n",
      " [1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 2, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 2, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 2, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 2, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n",
      "序列填充后的第一个元素：\n",
      " [   1   14   22   16   43  530  973 1622 1385   65  458    2   66 3941\n",
      "    4  173   36  256    5   25  100   43  838  112   50  670    2    9\n",
      "   35  480  284    5  150    4  172  112  167    2  336  385   39    4\n",
      "  172    2 1111   17  546   38   13  447    4  192   50   16    6  147\n",
      " 2025   19   14   22    4 1920    2  469    4   22   71   87   12   16\n",
      "   43  530   38   76   15   13 1247    4   22   17  515   17   12   16\n",
      "  626   18    2    5   62  386   12    8  316    8  106    5    4 2223\n",
      "    2   16  480   66 3785   33    4  130   12   16   38  619    5   25\n",
      "  124   51   36  135   48   25 1415   33    6   22   12  215   28   77\n",
      "   52    5   14  407   16   82    2    8    4  107  117    2   15  256\n",
      "    4    2    7 3766    5  723   36   71   43  530  476   26  400  317\n",
      "   46    7    4    2 1029   13  104   88    4  381   15  297   98   32\n",
      " 2071   56   26  141    6  194    2   18    4  226   22   21  134  476\n",
      "   26  480    5  144   30    2   18   51   36   28  224   92   25  104\n",
      "    4  226   65   16   38 1334   88   12   16  283    5   16    2  113\n",
      "  103   32   15   16    2   19  178   32    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
      "    0    0    0    0    0    0    0    0]\n"
     ]
    }
   ],
   "source": [
    "print(\"序列填充前的第一个元素：\\n\",x_train[0])\n",
    "x_train=tf.keras.preprocessing.sequence.pad_sequences(x_train,padding='post',maxlen=400,truncating='post')\n",
    "x_test=tf.keras.preprocessing.sequence.pad_sequences(x_test,padding='post',maxlen=400,truncating='post')\n",
    "print(\"序列填充后的第一个元素：\\n\",x_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "109863b1-bda1-49e6-bdec-fdd11d5d011b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda33\\Lib\\site-packages\\keras\\src\\layers\\core\\embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)                │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ ?                           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                          │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ ?                           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)                │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ ?                           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                          │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ ?                           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Embedding(output_dim=32,input_dim=4000,input_length=400))\n",
    "model.add(tf.keras.layers.Dropout(0.3))\n",
    "model.add(tf.keras.layers.LSTM(32))\n",
    "model.add(tf.keras.layers.Dropout(0.3))\n",
    "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ea93b23-0e11-48db-8475-ff98c16b1e6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 83ms/step - accuracy: 0.5062 - loss: 0.6931 - val_accuracy: 0.5008 - val_loss: 0.6936\n",
      "Epoch 2/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 79ms/step - accuracy: 0.5063 - loss: 0.6925 - val_accuracy: 0.5010 - val_loss: 0.6936\n",
      "Epoch 3/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 78ms/step - accuracy: 0.5094 - loss: 0.6922 - val_accuracy: 0.4950 - val_loss: 0.6943\n",
      "Epoch 4/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 78ms/step - accuracy: 0.5067 - loss: 0.6916 - val_accuracy: 0.5026 - val_loss: 0.6946\n",
      "Epoch 5/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 78ms/step - accuracy: 0.5114 - loss: 0.6908 - val_accuracy: 0.5056 - val_loss: 0.6934\n",
      "Epoch 6/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 78ms/step - accuracy: 0.5178 - loss: 0.6899 - val_accuracy: 0.5164 - val_loss: 0.6908\n",
      "Epoch 7/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 78ms/step - accuracy: 0.5221 - loss: 0.6925 - val_accuracy: 0.5148 - val_loss: 0.6936\n",
      "Epoch 8/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m25s\u001b[0m 78ms/step - accuracy: 0.5322 - loss: 0.6899 - val_accuracy: 0.5044 - val_loss: 0.6900\n",
      "Epoch 9/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 78ms/step - accuracy: 0.5270 - loss: 0.6843 - val_accuracy: 0.5030 - val_loss: 0.7050\n",
      "Epoch 10/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 78ms/step - accuracy: 0.5335 - loss: 0.6825 - val_accuracy: 0.5366 - val_loss: 0.6798\n",
      "391/391 - 11s - 27ms/step - accuracy: 0.5299 - loss: 0.6833\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6833421587944031, 0.5298799872398376]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "history=model.fit(x_train,y_train,batch_size=64,epochs=10,validation_split=0.2)\n",
    "model.evaluate(x_test,y_test,batch_size=64,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a2fdc12-b4fa-417e-98c3-f31777b3e69c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x280000bbb10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss=history.history['loss']\n",
    "acc=history.history['accuracy']\n",
    "val_loss=history.history['val_loss']\n",
    "val_acc=history.history['val_accuracy']\n",
    "plt.figure(figsize=(10,3))\n",
    "plt.subplot(121)\n",
    "plt.plot(loss,color='b',label='train')\n",
    "plt.plot(val_loss,color='r',label='validate')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.subplot(122)\n",
    "plt.plot(acc,color='b',label='train')\n",
    "plt.plot(val_acc,color='r',label='validate')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3ed31b17-7221-443f-bd9d-d7cd2c04307b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "评论为: The ultimate story of frendship,of hope,and of life,and overcoming adversity.Iunderstand why so many class this as the best film of all time,it isn't mine,but I get it.If you haven't seen it,or haven't seen it for some time,you need to watch it,it's amazing.\n",
      "预测结果为: 正面评论\n"
     ]
    }
   ],
   "source": [
    "dict={0:\"正面评论\",1:\"负面评论\"}\n",
    "def display_predict(text):\n",
    "    token=tf.keras.preprocessing.text.Tokenizer(num_words=4000)\n",
    "    token.fit_on_texts(text)\n",
    "    input_seq=token.texts_to_sequences(text)\n",
    "    test_seq=tf.keras.preprocessing.sequence.pad_sequences(input_seq,padding='post',maxlen=400,truncating='post')\n",
    "    pred=model.predict(test_seq)\n",
    "    print(\"评论为:\",text)\n",
    "    print(\"预测结果为:\",dict[np.argmax(pred)])\n",
    "test_text=\"The ultimate story of frendship,of hope,and of life,and overcoming adversity.Iunderstand why so many class this as the best film of all time,it isn't mine,but I get it.If you haven't seen it,or haven't seen it for some time,you need to watch it,it's amazing.\"\n",
    "display_predict(test_text)"
   ]
  },
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   "cell_type": "code",
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   "id": "bd1e486b-33a4-4bc5-8b9e-63f105719276",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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